Addestrare e valutare il modello

Ora che hai gestito i valori mancanti e creato le variabili fittizie, è il momento di valutare le prestazioni del tuo modello!

L’insieme di dati attrition, insieme alle suddivisioni test e train, alla lr_recipe e al tuo logistic_model() dichiarato, sono già caricati per te.

Questo esercizio fa parte del corso

Feature Engineering in R

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Istruzioni dell'esercizio

Raggruppa modello e ricetta in un workflow.
Adatta (fit) il workflow ai dati di training.
Genera un data frame aumentato per la valutazione delle prestazioni.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Bundle model and recipe in workflow
lr_workflow <- ___() %>%
  add_model(lr_model) %>%
  add_recipe(lr_recipe)

# Fit workflow to the train data
lr_fit <- ___(lr_workflow, data = train)

# Generate an augmented data frame for performance assessment
lr_aug <- lr_fit %>% ___(test)

lr_aug %>% roc_curve(truth = Attrition, .pred_No) %>% autoplot()
bind_rows(lr_aug %>% roc_auc(truth = Attrition, .pred_No),          
          lr_aug %>% accuracy(truth = Attrition, .pred_class))

Modifica ed esegui il codice

Feature Engineering in R

IntermediárioNível de habilidade

4.8+

120 reviews

You’ll wrap up the course by learning about feature engineering and machine learning techniques. You’ll begin by focusing on the problems associated with using all available features in a model and the importance of identifying irrelevant and redundant features and learning to remove these features using embedded methods such as lasso and elastic-net. Next, you’ll explore shrinkage methods such as lasso, ridge, and elastic-net, which can be used to regularize feature weights or select features by setting coefficients to zero. Finally, you’ll finish by focusing on creating an end-to-end feature engineering workflow and reviewing and practicing the previously learned concepts and functions in a small project.

Exercise 1: Reducing the model's features Exercise 2: Sifting through variable importance Exercise 3: Assessing model performance using all available predictors Exercise 4: Building a reduced model Exercise 5: Shrinkage methods Exercise 6: Manual regularization with Lasso Exercise 7: Tuning the penalty Exercise 8: Finalizing the model Exercise 9: Putting it all together Exercise 10: Prep and split Exercise 11: Preprocess Exercise 12: Model Exercise 13: Assess Exercise 14: Congratulations!